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Challenge 1: Visualizing Distributions | Seaborn
Data Science Interview Challenge
course content

Course Content

Data Science Interview Challenge

Data Science Interview Challenge

1. Python
2. NumPy
3. Pandas
4. Matplotlib
5. Seaborn
6. Statistics
7. Scikit-learn

bookChallenge 1: Visualizing Distributions

Understanding how data is distributed is fundamental in the data analysis process. Distributions help us to visualize the central tendencies, variability, and the presence of any outliers in our dataset. Seaborn, a statistical plotting library built on top of Matplotlib, provides a suite of tools that makes visualizing distributions a breeze.

The various plots and tools under Seaborn's distribution utilities can:

  • Examine the distribution of a dataset.
  • Visualize the relationship between multiple variables.
  • Display the underlying probability distributions of datasets.

Using Seaborn to create distribution plots ensures that the viewer can get a comprehensive view of the data's distribution and its characteristics.

Task
test

Swipe to show code editor

Using Seaborn, visualize the distribution of a dataset:

  1. Plot a univariate distribution of data using a histogram and overlay it with a kernel density estimate (KDE).
  2. Visualize the bivariate distribution between two variables using a scatter plot and include a KDE plot to see the data's density.

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Section 5. Chapter 1
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bookChallenge 1: Visualizing Distributions

Understanding how data is distributed is fundamental in the data analysis process. Distributions help us to visualize the central tendencies, variability, and the presence of any outliers in our dataset. Seaborn, a statistical plotting library built on top of Matplotlib, provides a suite of tools that makes visualizing distributions a breeze.

The various plots and tools under Seaborn's distribution utilities can:

  • Examine the distribution of a dataset.
  • Visualize the relationship between multiple variables.
  • Display the underlying probability distributions of datasets.

Using Seaborn to create distribution plots ensures that the viewer can get a comprehensive view of the data's distribution and its characteristics.

Task
test

Swipe to show code editor

Using Seaborn, visualize the distribution of a dataset:

  1. Plot a univariate distribution of data using a histogram and overlay it with a kernel density estimate (KDE).
  2. Visualize the bivariate distribution between two variables using a scatter plot and include a KDE plot to see the data's density.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 5. Chapter 1
toggle bottom row

bookChallenge 1: Visualizing Distributions

Understanding how data is distributed is fundamental in the data analysis process. Distributions help us to visualize the central tendencies, variability, and the presence of any outliers in our dataset. Seaborn, a statistical plotting library built on top of Matplotlib, provides a suite of tools that makes visualizing distributions a breeze.

The various plots and tools under Seaborn's distribution utilities can:

  • Examine the distribution of a dataset.
  • Visualize the relationship between multiple variables.
  • Display the underlying probability distributions of datasets.

Using Seaborn to create distribution plots ensures that the viewer can get a comprehensive view of the data's distribution and its characteristics.

Task
test

Swipe to show code editor

Using Seaborn, visualize the distribution of a dataset:

  1. Plot a univariate distribution of data using a histogram and overlay it with a kernel density estimate (KDE).
  2. Visualize the bivariate distribution between two variables using a scatter plot and include a KDE plot to see the data's density.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Understanding how data is distributed is fundamental in the data analysis process. Distributions help us to visualize the central tendencies, variability, and the presence of any outliers in our dataset. Seaborn, a statistical plotting library built on top of Matplotlib, provides a suite of tools that makes visualizing distributions a breeze.

The various plots and tools under Seaborn's distribution utilities can:

  • Examine the distribution of a dataset.
  • Visualize the relationship between multiple variables.
  • Display the underlying probability distributions of datasets.

Using Seaborn to create distribution plots ensures that the viewer can get a comprehensive view of the data's distribution and its characteristics.

Task
test

Swipe to show code editor

Using Seaborn, visualize the distribution of a dataset:

  1. Plot a univariate distribution of data using a histogram and overlay it with a kernel density estimate (KDE).
  2. Visualize the bivariate distribution between two variables using a scatter plot and include a KDE plot to see the data's density.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 5. Chapter 1
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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